Constrained data-driven optimal iterative learning control

被引:76
作者
Chi, Ronghu [1 ]
Liu, Xiaohe [1 ]
Zhang, Ruikun [2 ]
Hou, Zhongsheng [3 ]
Huang, Biao [4 ]
机构
[1] Qingdao Univ Sci & Technol, Sch Automat & Elect Engn, Qingdao 266042, Peoples R China
[2] Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266042, Peoples R China
[3] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Adv Control Syst Lab, Beijing 100044, Peoples R China
[4] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada
基金
美国国家科学基金会;
关键词
Data-driven control; Iterative learning control; Constrained nonlinear systems; Quadraticprogramming; Point-to-point tracking tasks; DISCRETE-TIME-SYSTEMS; NONLINEAR-SYSTEMS; OPTIMIZATION PROBLEMS; INPUT SATURATION; DYNAMIC-SYSTEMS; BATCH PROCESSES; LINEAR-SYSTEMS; DESIGN; UNCERTAINTIES; FRAMEWORK;
D O I
10.1016/j.jprocont.2017.03.003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A constrained optimal ILC for a class of nonlinear and non-affine systems, without requiring any explicit model information except for the input and output data, is proposed in this work. In order to address the nonlinearities, an iterative dynamic linearization method without omitting any information of the original plant is introduced in the iteration direction. The derived linearized data model is equivalent to the original nonlinear system and reflects the real-time dynamics of the controlled plant, rather than a static approximate model. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear matrix inequality, a novel constrained data-driven optimal ILC is developed by minimizing a predesigned objective function. The optimal learning gain is unfixed and updated iteratively according to the input and output measurements, which enhances the flexibility regarding modifications and expansions of the controlled plant. The results are further extended to the point-to-point control tasks where the exact tracking performance is required only at certain points and a constrained data-driven optimal point-to-point ILC is proposed by only utilizing the error measurements at the specified points only. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:10 / 29
页数:20
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